Interpretive Summary: Patterns of crop response to soil factors affecting productivity can be used as a basis for delineating crop management zones for precision agriculture management. Crop response can be quantified with the leaf area index (LAI) measure of foliage density. The spatial patterns of LAI variability can be more important than absolute accuracy of LAI at specific points for management zone delineation. Traditional methods for mapping LAI from remotely sensed imagery use only image spectral reflectance information and do not fully exploit image spatial pattern information. Two spatial-spectral approaches were investigated using airborne imagery aggregated to 2m x 2m, 4m x 4m, and 8m x 8m resolution cells. Spatial variance quantified with geostatistical semivariogram parameters calculated from imagery was applied to ground-based LAI measurements, and ground-based LAI measurements were co-kriged with co-located image pixels. The resulting LAI maps were compared with the ground-based LAI measurements interpolated using geostatistical kriging and LAI maps produced using regressions between the ground-based LAI and image reflectance transformed to a vegetation index. Approximation of the semivariogram of ground-based LAI measurements with a semivariogram from a remotely sensed image can provide reasonable results when used to interpolate LAI measurements. Maps of LAI from ground-based LAI measurements interpolated with image-derived kriging parameters were roughly comparable to results obtained with ordinary kriging of the ground-based LAI measurements with similar gross spatial trends of high, medium and low LAI for both sets of products. The ability of image-derived semivariogram to approximate the semivariogram of surface-based ground truth LAI is unfortunately still limited by the strength of the LAI-image reflectance relationship. Successful development of high spatial resolution LAI mapping procedures will be benefit producers and agricultural consultants seeking to stratify production fields into homogeneous zones. Given such zones, agrochemical applications can be optimized for the soil conditions thus potentially reducing the cost of production and reducing excess applications resulting in chemical losses to surrounding ecosystems and watersheds.

Technical Abstract:
Crop response zones quantified with leaf area index (LAI) provide decision-making criteria for delineation of crop management zones. The spatial pattern of LAI variability can be more important than absolute accuracy of LAI at specific points for this objective. Traditional methods for mapping LAI from remotely sensed data use image spectral information and fail to fully exploit image spatial information. Two spatial-spectral approaches were investigated using airborne data aggregated to 2m x 2m, 4m x 4m, and 8m x 8m resolution cells. Spatial variance derived from a spectral vegetation index (SVI) image was applied to ground-based LAI measurements, and ground-based LAI measurements were co-kriged with co-located image SVI pixels. The resulting LAI maps were compared with the ground-based LAI measurements interpolated with point kriging, and LAI maps produced using regressions between the ground-based LAI and the SVI. Approximation of the semivariogram of ground-based measurements of LAI with a semivariogram from a remotely sensed image can provide reasonable results when used to interpolate LAI measurements. Maps of LAI from ground-based LAI measurements interpolated with image-derived kriging parameters were roughly comparable to results obtained with ordinary kriging of the ground-based LAI measurements with similar gross spatial trends of high, medium and low LAI for both sets of products. The ability of an image-derived semivariogram to approximate the semivariogram of surface-based ground truth LAI is unfortunately still limited by the strength of the LAI-SVI relationship. Given the continuous spatial coverage of imagery, the image-derived patterns may be more representative of LAI variability than the ground truth samples. Analysis of shape and spatial patterns is qualitative.